Most decision problems require tradeoffs between conflicting objectives. The aim of multiobjective optimization is to define the Pareto optimal set of solutions that is to be used in the trade-off process. Recently several heuristic methods have been developed to find the Pareto set; these include the ε-dominance multiobjective evolutionary algorithm (εMOEA) and the multiobjective ant colony optimization (MOACO) algorithm. In this study a new MOACO method is proposed to overcome the shortcomings of earlier MOACO algorithms. This method is more robust, uses fewer tuning parameters, and is independent of the number of objectives and independent of the problem. The key improvements involve only updating pheromone on non-dominated solutions, using a time decay factor to avoid excessive pheromone build-up and switching off heuristic information. A case study based on stormwater harvesting for potable reuse illustrates the superiority of the new MOACO method. Benchmarking the new MOACO against εMOEA revealed similar performance. Both produced similar diversity in Pareto sets. εMOEA tended to dominate MOACO solutions for function evaluations up to 10,000 with MOACO then becoming dominant.